Clearance of myoglobin by high cutoff continuous veno‐venous hemodialysis in a patient with rhabdomyolysis: A case report
Bibliographic record
Abstract
Continuous veno-venous hemodialysis using high cutoff filters (HCO-CVVHD) is a promising technique, which may be effective to decrease the extremely high level of circulating myoglobin in patients with rhabdomyolysis (RM). Here, we report a patient with RM caused by heat stroke who was successfully treated by HCO-CVVHD. A male patient received HCO-CVVHD with 4 L/h dialysate for 5 days and then pre-dilution continuous veno-venous hemofiltration (CVVH) at a dose of 4 L/h until recovery of renal function. The clearance of myoglobin and albumin at 5 minutes, and at 4, 12, and 24 hours were calculated. The serum myoglobin level decreased from a peak of 25,400 ng/mL on admission to 133 ng/mL at discharge. During HCO-CVVHD, the mean clearances of serum myoglobin at four timepoints were 61.3 (range, 61.0-61.6), 52.3 (38.9-65.8), 47.3 (46.8-47.9), and 43.7 (39.5-48.0) mL/min, respectively, and the mean clearances of albumin were 12.4 (range, 11.8-13.1), 3.1 (2.5-3.8), 1.2 (1.0-1.4), and 0.8 (0.6-1.0) mL/min, respectively. During CVVH, the clearance rates of myoglobin at 5 minutes and 24 hours were 17.0 and 3.8 mL/min, respectively, with a negligible clearance of albumin. HCO-CVVHD can effectively decrease serum myoglobin in patients with RM because of much higher clearance of myoglobin than CVVH. However, attention should be paid to albumin loss during HCO-CVVHD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".